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      <a href="/blog/2014/06/23/the-days-in-the-pit/" title="阿里推荐大赛：那些在坑里翻滚的日子" itemprop="url">阿里推荐大赛：那些在坑里翻滚的日子</a>
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		<a href="https://plus.google.com/111190881341800841449?rel=author" title="WuChong" target="_blank" itemprop="author">WuChong</a>
		
  <p class="article-time">
    <time datetime="2014-06-23T15:02:03.000Z" itemprop="datePublished"> 发表于 2014-06-23</time>
    
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        <p>晚上提交完这次的结果后，我知道明天早上又会有一百支一起奋斗过的团队们要离开了。而这一次的月星星与我还是那么的遥不可及。回想起这段日子每天早上打开排行榜就像开足彩一样紧张而兴奋。想想多年后是不是会怀念这段时光，所以决定还是写点东西吧。</p>
<h2 id="前奏">前奏</h2>
<p>4月25日正式开启了 Season2 的征途，队员们纷纷进入天池系统开始大展身手。人们总是热于尝试新鲜事物，就好像 Swift 刚发布的数小时内就有开发者重写了 Flappy Bird，这种激情是值得敬畏的。我也在比赛开始一星期内写了两篇 ODPS SQL 入门的文章，当时随便想几个规则就能进排行榜。之后我们用 SQL 实现了第一季的所有规则，后来还尝试了线性回归、逻辑回归，但是效果都不好，成绩停留在 4% 出头点。</p>
<p>到了5月10号左右，由于实验室的多个项目紧逼，逐渐开始淡出比赛。当然在第一个月的淘汰期由于压根不在比赛，所以完全没有感受到比分的残暴。直到这一次深有体会。幸好那时候还没被淘汰。<br>
        
        
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      <a href="/blog/2014/04/29/odps-sql-build-offline-evaluation/" title="阿里推荐大赛：ODPS SQL 构建离线评估" itemprop="url">阿里推荐大赛：ODPS SQL 构建离线评估</a>
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		<a href="https://plus.google.com/111190881341800841449?rel=author" title="WuChong" target="_blank" itemprop="author">WuChong</a>
		
  <p class="article-time">
    <time datetime="2014-04-29T13:11:33.000Z" itemprop="datePublished"> 发表于 2014-04-29</time>
    
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        <p><a href="http://wuchong.me/blog/2014/04/26/odps-sql-introduction/" target="_blank" rel="external">上一篇文章</a>介绍了 ODPS SQL 的大概使用方法，几个 tips，和讲到一半的离线评估。现在上来把上次的坑填完。希望对于还没有开始离线调优的团队有点帮助。</p>
<h2 id="划分训练集、验证集">划分训练集、验证集</h2>
<p>回顾训练集、验证集的划分。</p>
<p>根据时间，可以将前三月划分为训练集：</p>
<figure class="highlight sql"><table><tr><td class="gutter"><pre>1
2
3
4
</pre></td><td class="code"><pre><span class="operator"><span class="keyword">create</span> <span class="keyword">table</span> train_set <span class="keyword">as</span>
<span class="keyword">select</span> * 
<span class="keyword">from</span> t_alibaba_bigdata_user_brand_total_1
<span class="keyword">where</span> visit_datetime &lt;= <span class="string">'07-15'</span>;</span>
</pre></td></tr></table></figure>

<p>同理，验证集如下，注意去重：</p>
<figure class="highlight sql"><table><tr><td class="gutter"><pre>1
2
3
4
5
6
7
8
9
10
</pre></td><td class="code"><pre><span class="operator"><span class="keyword">create</span> <span class="keyword">table</span> validate_set <span class="keyword">as</span>
<span class="keyword">select</span>
    user_id,
    wm_concat(<span class="string">','</span>,brand_id) <span class="keyword">as</span> brand
<span class="keyword">from</span>(
    <span class="keyword">select</span> <span class="keyword">distinct</span> user_id,brand_id
    <span class="keyword">from</span> t_alibaba_bigdata_user_brand_total_1
    <span class="keyword">where</span> type = <span class="string">'1'</span> <span class="keyword">and</span> visit_datetime &gt; <span class="string">'07-15'</span>
)a
<span class="keyword">group</span> <span class="keyword">by</span> user_id;</span>
</pre></td></tr></table></figure>

<p>这里<code>distinct</code>关键字的作用就是去重，当然也可以用<code>group by</code>实现。两者效果一样，那效率呢？在 MySQL 中（如果没记错的话，天池的底层数据库是基于 MySQL 的），两者的实现原理很相似。区别在于<code>distinct</code>会读取所有的记录，而<code>group by</code>是在分组之后每组只返回一条记录，也就是说后者读取的条数要少很多，效率会更高一些。所以可以用以下代码代替上述的子查询：</p>
<figure class="highlight"><table><tr><td class="gutter"><pre>1
2
3
4
</pre></td><td class="code"><pre><span class="keyword">select</span> user_id,brand_id
<span class="keyword">from</span> t_alibaba_bigdata_user_brand_total_1
<span class="keyword">where</span> <span class="keyword">type</span> = <span class="string">'1'</span> <span class="keyword">and</span> visit_datetime &gt; <span class="string">'07-15'</span>
<span class="keyword">group</span> <span class="keyword">by</span> user_id,brand_id
</pre></td></tr></table></figure>

<p>扯远了… 回来吧。计算推荐集、验证集条数的方法之前也给出了：</p>
<figure class="highlight sql"><table><tr><td class="gutter"><pre>1
</pre></td><td class="code"><pre><span class="operator"><span class="keyword">select</span> <span class="aggregate">sum</span>(regexp_count(brand,<span class="string">','</span>)+<span class="number">1</span>) <span class="keyword">from</span> t_tmall_add_user_brand_predict_dh;</span>
</pre></td></tr></table></figure>

<p>接下来要计算推荐集和验证集之间的重复条数，这里可以用 UDF 方便地实现。<br>
        
        
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      <a href="/blog/2014/04/26/odps-sql-introduction/" title="阿里推荐大赛：ODPS SQL 入门" itemprop="url">阿里推荐大赛：ODPS SQL 入门</a>
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		<a href="https://plus.google.com/111190881341800841449?rel=author" title="WuChong" target="_blank" itemprop="author">WuChong</a>
		
  <p class="article-time">
    <time datetime="2014-04-26T03:14:32.000Z" itemprop="datePublished"> 发表于 2014-04-26</time>
    
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        <p>第二季才是真正的开始，第一季的汗水、喜悦、纷争都通通过去了。第二季绝对是拼算法、拼模型、拼平台熟练度的比赛，相信坚持下来的话肯定收获颇丰。昨天也迫不及待地进入 「天池」 ，居然是 Windows 系统，一点都提升不了逼格啊。看文档的过程中，又是 ODPS SQL、MapReduce、XLab/XLib、Tunnel、ODPS SDK什么的，相信很多人都跟我一样看的眼花，茫然不知所措，更别提对 MR 不熟悉、SQL 忘地差不多、Java 不熟练的同学了。 到底先学哪个？到底哪个实现模型算法更快？</p>
<p>文档里推荐的开发流程是这样的：</p>
<blockquote>
<ol>
<li>首先看能否使用 ODPS SQL 搞定，能的话尽量用 SQL，简单，不容易出错；</li>
<li>编写 M/R 代码，使用本地调试功能进行基本的测试；</li>
<li>编写 M/R 单元测试用例，ODPS M/R 提供了 MRUnitTest 方便编写单元测试用例；</li>
<li>进行集群调试，验证结果。</li>
</ol>
</blockquote>
<p>所以从 SQL 入门应该是学习曲线最快的，而且也能方便进行分析，快速实现模型，快速提交结果。</p>
<p>如果您已经看过文档了，小弟觉得就可以跳过下面的啰嗦的内容了 ：）<br>
        
        
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      <a href="/blog/2014/04/24/easy-web-scraping-with-python/" title="使用 Python 轻松抓取网页" itemprop="url">使用 Python 轻松抓取网页</a>
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		<a href="https://plus.google.com/111190881341800841449?rel=author" title="WuChong" target="_blank" itemprop="author">WuChong</a>
		
  <p class="article-time">
    <time datetime="2014-04-24T15:04:00.000Z" itemprop="datePublished"> 发表于 2014-04-24</time>
    
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        <p>[ 翻译自英文原文：<a href="http://blog.miguelgrinberg.com/post/easy-web-scraping-with-python" target="_blank" rel="external">Easy Web Scraping with Python</a> ]</p>
<p>一年多以前我写了一篇文章<a href="http://blog.miguelgrinberg.com/post/easy-web-scraping-with-nodejs" target="_blank" rel="external">「web scraping using Node.js」</a>。今天我重新回顾了这个话题，但是这一次我将使用 Python，这样这两种语言所提供的技术就能进行对比和比较。</p>
<h2 id="问题">问题</h2>
<p>我敢肯定你知道，我在本月初参加了在蒙特利尔举办的 PyCon 大会。所有的演讲和教程的视频都已经发布到 YouTube 上了，目录在 <a href="http://pyvideo.org/category/50/pycon-us-2014" target="_blank" rel="external">pyvideo.org</a>。</p>
<p>我认为知道这个大会上的哪些视频最受欢迎将会是非常有用的，所以我们将要写一个爬虫脚本。这个脚本将会从 pyvideo.org 上获取有效的视频列表，然后从每个 YouTube 页面获取观看的统计数据。听起来很有趣？让我们开始吧！</p>
<h2 id="工具">工具</h2>
<p>在抓取网站中有两个基本的任务：</p>
<ol>
<li>加载网页到一个 string 里。</li>
<li>从网页中解析 HTML 来定位感兴趣的位置。</li>
</ol>
<p>Python 为上面两个任务提供了两个超棒的工具。我将使用 <a href="http://docs.python-requests.org/en/latest/" target="_blank" rel="external">requests</a> 去加载网页，用 <a href="http://www.crummy.com/software/BeautifulSoup/bs4/doc/" target="_blank" rel="external">BeautifulSoup</a> 去做解析。</p>
<p>我们可以把上面两个包放到一个虚拟环境：</p>
<figure class="highlight"><table><tr><td class="gutter"><pre>1
2
3
4
</pre></td><td class="code"><pre><span class="variable">$ </span>mkdir pycon-scraper
<span class="variable">$ </span>virtualenv venv
<span class="variable">$ </span>source venv/bin/activate
(venv) <span class="variable">$ </span>pip install requests beautifulsoup4
</pre></td></tr></table></figure><br>如果使用的是 Windows 操作系统，注意上面虚拟环境的激活命令是不同的，你应该使用<code>venv\Scripts\activate</code>。<br>
        
        
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      <a href="/blog/2014/04/19/recsys-cf-study/" title="推荐系统学习：协同过滤实现" itemprop="url">推荐系统学习：协同过滤实现</a>
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  <p class="article-time">
    <time datetime="2014-04-18T17:07:15.000Z" itemprop="datePublished"> 发表于 2014-04-19</time>
    
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        <p>阿里大数据竞赛的第一季也即将告一段落，之前一直想发篇竞赛心得/攻略，但是看到有人发的博客居然被阿里查封了就不敢了…扯远了，这篇文章主要总结下自己看了项亮的《推荐系统实践》后的学习笔记。作为入门，这么书确实写的不错。</p>
<h2 id="推荐系统的评测指标">推荐系统的评测指标</h2>
<p>为了评估推荐算法的好坏需要各方面的评估指标。</p>
<ul>
<li><p>准确率<br>准确率就是最终的推荐列表中有多少是推荐对了的。</p>
</li>
<li><p>召回率<br>召回率就是推荐对了的占全集的多少。<br>下图直观地描述了准确率和召回率的含义<br><img src="http://ww2.sinaimg.cn/large/81b78497jw1efj1yg6uywj20kg0cm778.jpg" alt=""></p>

        
        
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